Subtracted diversity array identifies novel molecular markers including retrotransposons for fingerprinting echinacea species
Bibliographic record
Abstract
Echinacea, native to the Canadian prairies and the prairie states of the United States, has a long tradition as a folk medicine for the Native Americans. Currently, Echinacea are among the top 10 selling herbal medicines in the U.S. and Europe, due to increasing popularity for the treatment of common cold and ability to stimulate the immune system. However, the genetic relationship within the species of this genus is unclear, making the authentication of the species used for the medicinal industry more difficult. We report the construction of a novel Subtracted Diversity Array (SDA) for Echinacea species and demonstrate the potential of this array for isolating highly polymorphic sequences. In order to selectively isolate Echinaceaspecific sequences, a Suppression Subtractive Hybridization (SSH) was performed between a pool of twenty-four Echinacea genotypes and a pool of other angiosperms and non-angiosperms. A total of 283 subtracted genomic DNA (gDNA) fragments were amplified and arrayed. Twenty-seven Echinacea genotypes including four that were not used in the array construction could be successfully discriminated. Interestingly, unknown samples of E. paradoxa and E. purpurea could be unambiguously identified from the cluster analysis. Furthermore, this Echinacea-specific SDA was also able to isolate highly polymorphic retrotransposon sequences. Five out of the eleven most discriminatory features matched to known retrotransposons. This is the first time retrotransposon sequences have been used to fingerprint Echinacea, highlighting the potential of retrotransposons as based molecular markers useful for fingerprinting and studying diversity patterns in Echinacea.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".